In silico Immunogenicity Assessment for Sequences Containing Unnatural Amino Acids: A Method Using Existing in silico Algorithm Infrastructure and a Vision for Future Enhancements.

The in silico prediction of T cell epitopes within any peptide or biologic drug candidate serves as an important first step for assessing immunogenicity. T cell epitopes bind human leukocyte antigen (HLA) by a well-characterized interaction of amino acid side chains and pockets in the HLA molecule binding groove. Immunoinformatics tools, such as the EpiMatrix algorithm, have been developed to screen natural amino acid sequences for peptides that will bind HLA. In addition to commonly occurring in synthetic peptide impurities, unnatural amino acids (UAA) are also often incorporated into novel peptide therapeutics to improve properties of the drug product. To date, the HLA binding properties of peptides containing UAA are not accurately estimated by most algorithms. Both scenarios warrant the need for enhanced predictive tools. The authors developed an in silico method for modeling the impact of a given UAA on a peptide's likelihood of binding to HLA and, by extension, its immunogenic potential. In silico assessment of immunogenic potential allows for risk-based selection of best candidate peptides in further confirmatory in vitro, ex vivo and in vivo assays, thereby reducing the overall cost of immunogenicity evaluation. Examples demonstrating in silico immunogenicity prediction for product impurities that are commonly found in formulations of the generic peptides teriparatide and semaglutide are provided. Next, this article discusses how HLA binding studies can be used to estimate the binding potentials of commonly encountered UAA and "correct" in silico estimates of binding based on their naturally occurring counterparts. As demonstrated here, these in vitro binding studies are usually performed with known ligands which have been modified to contain UAA in HLA anchor positions. An example using D-amino acids in relative binding position 1 (P1) of the PADRE peptide is presented. As more HLA binding data become available, new predictive models allowing for the direct estimation of HLA binding for peptides containing UAA can be established.

Identification and Immune Assessment of T Cell Epitopes in Five Plasmodium falciparum Blood Stage Antigens to Facilitate Vaccine Candidate Selection and Optimization.

The hurdles to effective blood stage malaria vaccine design include immune evasion tactics used by the parasite such as redundant invasion pathways and antigen variation among circulating parasite strains. While blood stage malaria vaccine development primarily focuses on eliciting optimal humoral responses capable of blocking erythrocyte invasion, clinically-tested Plasmodium falciparum (Pf) vaccines have not elicited sterile protection, in part due to the dramatically high levels of antibody needed. Recent development efforts with non-redundant, conserved blood stage antigens suggest both high antibody titer and rapid antibody binding kinetics are important efficacy factors. Based on the central role of helper CD4 T cells in development of strong, protective immune responses, we systematically analyzed the class II epitope content in five leading Pf blood stage antigens (RH5, CyRPA, RIPR, AMA1 and EBA175) using in silico, in vitro, and ex vivo methodologies. We employed in silico T cell epitope analysis to enable identification of 67 HLA-restricted class II epitope clusters predicted to bind a panel of nine HLA-DRB1 alleles. We assessed a subset of these for HLA-DRB1 allele binding in vitro, to verify the in silico predictions. All clusters assessed (40 clusters represented by 46 peptides) bound at least two HLA-DR alleles in vitro. The overall epitope prediction to in vitro HLA-DRB1 allele binding accuracy was 71%. Utilizing the set of RH5 class II epitope clusters (10 clusters represented by 12 peptides), we assessed stimulation of T cells collected from HLA-matched RH5 vaccinees using an IFN-γ T cell recall assay. All clusters demonstrated positive recall responses, with the highest responses - by percentage of responders and response magnitude - associated with clusters located in the N-terminal region of RH5. Finally, a statistically significant correlation between in silico epitope predictions and ex vivo IFN-γ recall response was found when accounting for HLA-DR matches between the epitope predictions and donor HLA phenotypes. This is the first comprehensive analysis of class II epitope content in RH5, CyRPA, RIPR, AMA1 and EBA175 accompanied by in vitro HLA binding validation for all five proteins and ex vivo T cell response confirmation for RH5.

Identification, Selection and Immune Assessment of Liver Stage CD8 T Cell Epitopes From Plasmodium falciparum.

Immunization with radiation-attenuated sporozoites (RAS) has been shown to protect against malaria infection, primarily through CD8 T cell responses, but protection is limited based on parasite strain. Therefore, while CD8 T cells are an ideal effector population target for liver stage malaria vaccine development strategies, such strategies must incorporate conserved epitopes that cover a large range of class I human leukocyte antigen (HLA) supertypes to elicit cross-strain immunity across the target population. This approach requires identifying and characterizing a wide range of CD8 T cell epitopes for incorporation into a vaccine such that coverage across a large range of class I HLA alleles is attained. Accordingly, we devised an experimental framework to identify CD8 T cell epitopes from novel and minimally characterized antigens found at the pre-erythrocytic stage of parasite development. Through in silico analysis we selected conserved P. falciparum proteins, using P. vivax orthologues to establish stringent conservation parameters, predicted to have a high number of T cell epitopes across a set of six class I HLA alleles representative of major supertypes. Using the decision framework, five proteins were selected based on the density and number of predicted epitopes. Selected epitopes were synthesized as peptides and evaluated for binding to the class I HLA alleles in vitro to verify in silico binding predictions, and subsequently for stimulation of human T cells using the Modular IMmune In-vitro Construct (MIMIC®) technology to verify immunogenicity. By combining the in silico tools with the ex vivo high throughput MIMIC platform, we identified 15 novel CD8 T cell epitopes capable of stimulating an immune response in alleles across the class I HLA panel. We recommend these epitopes should be evaluated in appropriate in vivo humanized immune system models to determine their protective efficacy for potential inclusion in future vaccines.